Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations10
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory392.0 B
Average record size in memory39.2 B

Variable types

Categorical9
Numeric17

Alerts

310 has constant value "0" Constant
0 has constant value "0" Constant
167 has constant value "0" Constant
104 is highly overall correlated with 135 and 8 other fieldsHigh correlation
135 is highly overall correlated with 104 and 5 other fieldsHigh correlation
150 is highly overall correlated with 104 and 9 other fieldsHigh correlation
16 is highly overall correlated with 104 and 8 other fieldsHigh correlation
204 is highly overall correlated with 104 and 11 other fieldsHigh correlation
233 is highly overall correlated with 150 and 7 other fieldsHigh correlation
330 is highly overall correlated with 536 and 7 other fieldsHigh correlation
353 is highly overall correlated with 150 and 7 other fieldsHigh correlation
368 is highly overall correlated with 104 and 8 other fieldsHigh correlation
381 is highly overall correlated with 353 and 6 other fieldsHigh correlation
485 is highly overall correlated with 150 and 7 other fieldsHigh correlation
536 is highly overall correlated with 330 and 5 other fieldsHigh correlation
544 is highly overall correlated with 150 and 10 other fieldsHigh correlation
576 is highly overall correlated with 204 and 9 other fieldsHigh correlation
581 is highly overall correlated with 104 and 6 other fieldsHigh correlation
59 is highly overall correlated with 104 and 11 other fieldsHigh correlation
590 is highly overall correlated with 330 and 2 other fieldsHigh correlation
619 is highly overall correlated with 104 and 8 other fieldsHigh correlation
632 is highly overall correlated with 104 and 9 other fieldsHigh correlation
648 is highly overall correlated with 16 and 6 other fieldsHigh correlation
668 is highly overall correlated with 330 and 7 other fieldsHigh correlation
673 is highly overall correlated with 135 and 3 other fieldsHigh correlation
label is highly overall correlated with 150 and 4 other fieldsHigh correlation
59 is highly imbalanced (53.1%) Imbalance
485 has unique values Unique
576 has unique values Unique
668 has 5 (50.0%) zeros Zeros
632 has 3 (30.0%) zeros Zeros
381 has 2 (20.0%) zeros Zeros
233 has 1 (10.0%) zeros Zeros
104 has 4 (40.0%) zeros Zeros
330 has 5 (50.0%) zeros Zeros
353 has 2 (20.0%) zeros Zeros
648 has 5 (50.0%) zeros Zeros
485 has 1 (10.0%) zeros Zeros
150 has 4 (40.0%) zeros Zeros
204 has 5 (50.0%) zeros Zeros
135 has 4 (40.0%) zeros Zeros
581 has 2 (20.0%) zeros Zeros
536 has 4 (40.0%) zeros Zeros
544 has 2 (20.0%) zeros Zeros
label has 3 (30.0%) zeros Zeros

Reproduction

Analysis started2025-03-23 14:43:53.584587
Analysis finished2025-03-23 14:44:08.914239
Duration15.33 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

310
Categorical

Constant 

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size712.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10
100.0%

Length

2025-03-23T20:14:08.966449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T20:14:09.019013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 10
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10
100.0%

668
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.5
Minimum0
Maximum229
Zeros5
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:09.054078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q3148.5
95-th percentile212.35
Maximum229
Range229
Interquartile range (IQR)148.5

Descriptive statistics

Standard deviation91.170475
Coefficient of variation (CV)1.2404146
Kurtosis-1.1846983
Mean73.5
Median Absolute Deviation (MAD)25
Skewness0.77129244
Sum735
Variance8312.0556
MonotonicityNot monotonic
2025-03-23T20:14:09.093408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5
50.0%
192 1
 
10.0%
99 1
 
10.0%
229 1
 
10.0%
50 1
 
10.0%
165 1
 
10.0%
ValueCountFrequency (%)
0 5
50.0%
50 1
 
10.0%
99 1
 
10.0%
165 1
 
10.0%
192 1
 
10.0%
229 1
 
10.0%
ValueCountFrequency (%)
229 1
 
10.0%
192 1
 
10.0%
165 1
 
10.0%
99 1
 
10.0%
50 1
 
10.0%
0 5
50.0%

59
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size712.0 B
0
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9
90.0%
1 1
 
10.0%

Length

2025-03-23T20:14:09.156652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T20:14:09.194363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 9
90.0%
1 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 9
90.0%
1 1
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9
90.0%
1 1
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9
90.0%
1 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9
90.0%
1 1
 
10.0%

632
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127
Minimum0
Maximum233
Zeros3
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:09.229987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121.75
median163.5
Q3206
95-th percentile226.7
Maximum233
Range233
Interquartile range (IQR)184.25

Descriptive statistics

Standard deviation97.259104
Coefficient of variation (CV)0.76581972
Kurtosis-1.7433968
Mean127
Median Absolute Deviation (MAD)62.5
Skewness-0.47038447
Sum1270
Variance9459.3333
MonotonicityNot monotonic
2025-03-23T20:14:09.281503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3
30.0%
194 1
 
10.0%
87 1
 
10.0%
210 1
 
10.0%
137 1
 
10.0%
219 1
 
10.0%
190 1
 
10.0%
233 1
 
10.0%
ValueCountFrequency (%)
0 3
30.0%
87 1
 
10.0%
137 1
 
10.0%
190 1
 
10.0%
194 1
 
10.0%
210 1
 
10.0%
219 1
 
10.0%
233 1
 
10.0%
ValueCountFrequency (%)
233 1
 
10.0%
219 1
 
10.0%
210 1
 
10.0%
194 1
 
10.0%
190 1
 
10.0%
137 1
 
10.0%
87 1
 
10.0%
0 3
30.0%

0
Categorical

Constant 

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size712.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10
100.0%

Length

2025-03-23T20:14:09.340725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T20:14:09.362211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 10
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10
100.0%

381
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.7
Minimum0
Maximum240
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:09.409921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q192
median192.5
Q3214.5
95-th percentile234.6
Maximum240
Range240
Interquartile range (IQR)122.5

Descriptive statistics

Standard deviation92.014552
Coefficient of variation (CV)0.62722939
Kurtosis-0.97027122
Mean146.7
Median Absolute Deviation (MAD)41.5
Skewness-0.80231389
Sum1467
Variance8466.6778
MonotonicityNot monotonic
2025-03-23T20:14:09.463234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2
20.0%
240 1
10.0%
204 1
10.0%
104 1
10.0%
88 1
10.0%
218 1
10.0%
190 1
10.0%
195 1
10.0%
228 1
10.0%
ValueCountFrequency (%)
0 2
20.0%
88 1
10.0%
104 1
10.0%
190 1
10.0%
195 1
10.0%
204 1
10.0%
218 1
10.0%
228 1
10.0%
240 1
10.0%
ValueCountFrequency (%)
240 1
10.0%
228 1
10.0%
218 1
10.0%
204 1
10.0%
195 1
10.0%
190 1
10.0%
104 1
10.0%
88 1
10.0%
0 2
20.0%

233
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.4
Minimum0
Maximum254
Zeros1
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:09.514524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q11
median124.5
Q3213.25
95-th percentile241.85
Maximum254
Range254
Interquartile range (IQR)212.25

Descriptive statistics

Standard deviation107.77672
Coefficient of variation (CV)0.93394042
Kurtosis-2.09869
Mean115.4
Median Absolute Deviation (MAD)113
Skewness-0.0202842
Sum1154
Variance11615.822
MonotonicityNot monotonic
2025-03-23T20:14:09.552319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3
30.0%
205 1
 
10.0%
93 1
 
10.0%
156 1
 
10.0%
254 1
 
10.0%
216 1
 
10.0%
227 1
 
10.0%
0 1
 
10.0%
ValueCountFrequency (%)
0 1
 
10.0%
1 3
30.0%
93 1
 
10.0%
156 1
 
10.0%
205 1
 
10.0%
216 1
 
10.0%
227 1
 
10.0%
254 1
 
10.0%
ValueCountFrequency (%)
254 1
 
10.0%
227 1
 
10.0%
216 1
 
10.0%
205 1
 
10.0%
156 1
 
10.0%
93 1
 
10.0%
1 3
30.0%
0 1
 
10.0%

104
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.6
Minimum0
Maximum235
Zeros4
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:09.599585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24.5
Q3164
95-th percentile219.25
Maximum235
Range235
Interquartile range (IQR)164

Descriptive statistics

Standard deviation95.708121
Coefficient of variation (CV)1.2176606
Kurtosis-1.4810079
Mean78.6
Median Absolute Deviation (MAD)24.5
Skewness0.67680328
Sum786
Variance9160.0444
MonotonicityNot monotonic
2025-03-23T20:14:09.646911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4
40.0%
4 1
 
10.0%
200 1
 
10.0%
125 1
 
10.0%
177 1
 
10.0%
235 1
 
10.0%
45 1
 
10.0%
ValueCountFrequency (%)
0 4
40.0%
4 1
 
10.0%
45 1
 
10.0%
125 1
 
10.0%
177 1
 
10.0%
200 1
 
10.0%
235 1
 
10.0%
ValueCountFrequency (%)
235 1
 
10.0%
200 1
 
10.0%
177 1
 
10.0%
125 1
 
10.0%
45 1
 
10.0%
4 1
 
10.0%
0 4
40.0%

330
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.2
Minimum0
Maximum251
Zeros5
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:09.702475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.5
Q3171
95-th percentile238.85
Maximum251
Range251
Interquartile range (IQR)171

Descriptive statistics

Standard deviation102.74759
Coefficient of variation (CV)1.2059576
Kurtosis-1.3704154
Mean85.2
Median Absolute Deviation (MAD)37.5
Skewness0.68372837
Sum852
Variance10557.067
MonotonicityNot monotonic
2025-03-23T20:14:09.751669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5
50.0%
224 1
 
10.0%
191 1
 
10.0%
251 1
 
10.0%
111 1
 
10.0%
75 1
 
10.0%
ValueCountFrequency (%)
0 5
50.0%
75 1
 
10.0%
111 1
 
10.0%
191 1
 
10.0%
224 1
 
10.0%
251 1
 
10.0%
ValueCountFrequency (%)
251 1
 
10.0%
224 1
 
10.0%
191 1
 
10.0%
111 1
 
10.0%
75 1
 
10.0%
0 5
50.0%

353
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.1
Minimum0
Maximum227
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:09.789201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q194.25
median202.5
Q3204
95-th percentile224.75
Maximum227
Range227
Interquartile range (IQR)109.75

Descriptive statistics

Standard deviation89.831262
Coefficient of variation (CV)0.61486148
Kurtosis-0.84587995
Mean146.1
Median Absolute Deviation (MAD)22
Skewness-0.91131553
Sum1461
Variance8069.6556
MonotonicityNot monotonic
2025-03-23T20:14:09.853592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
204 2
20.0%
0 2
20.0%
89 1
10.0%
203 1
10.0%
110 1
10.0%
222 1
10.0%
202 1
10.0%
227 1
10.0%
ValueCountFrequency (%)
0 2
20.0%
89 1
10.0%
110 1
10.0%
202 1
10.0%
203 1
10.0%
204 2
20.0%
222 1
10.0%
227 1
10.0%
ValueCountFrequency (%)
227 1
10.0%
222 1
10.0%
204 2
20.0%
203 1
10.0%
202 1
10.0%
110 1
10.0%
89 1
10.0%
0 2
20.0%

619
Categorical

High correlation 

Distinct5
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size718.0 B
0
193
5
242
240

Length

Max length3
Median length1
Mean length1.6
Min length1

Characters and Unicode

Total characters16
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)40.0%

Sample

1st row193
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6
60.0%
193 1
 
10.0%
5 1
 
10.0%
242 1
 
10.0%
240 1
 
10.0%

Length

2025-03-23T20:14:09.920046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T20:14:09.975416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6
60.0%
193 1
 
10.0%
5 1
 
10.0%
242 1
 
10.0%
240 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 7
43.8%
2 3
18.8%
4 2
 
12.5%
9 1
 
6.2%
1 1
 
6.2%
5 1
 
6.2%
3 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7
43.8%
2 3
18.8%
4 2
 
12.5%
9 1
 
6.2%
1 1
 
6.2%
5 1
 
6.2%
3 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7
43.8%
2 3
18.8%
4 2
 
12.5%
9 1
 
6.2%
1 1
 
6.2%
5 1
 
6.2%
3 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7
43.8%
2 3
18.8%
4 2
 
12.5%
9 1
 
6.2%
1 1
 
6.2%
5 1
 
6.2%
3 1
 
6.2%

648
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.6
Minimum0
Maximum229
Zeros5
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:10.028373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q3204.75
95-th percentile227.65
Maximum229
Range229
Interquartile range (IQR)204.75

Descriptive statistics

Standard deviation109.14333
Coefficient of variation (CV)1.2603156
Kurtosis-2.1421594
Mean86.6
Median Absolute Deviation (MAD)8
Skewness0.51970668
Sum866
Variance11912.267
MonotonicityNot monotonic
2025-03-23T20:14:10.075907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5
50.0%
212 1
 
10.0%
183 1
 
10.0%
226 1
 
10.0%
16 1
 
10.0%
229 1
 
10.0%
ValueCountFrequency (%)
0 5
50.0%
16 1
 
10.0%
183 1
 
10.0%
212 1
 
10.0%
226 1
 
10.0%
229 1
 
10.0%
ValueCountFrequency (%)
229 1
 
10.0%
226 1
 
10.0%
212 1
 
10.0%
183 1
 
10.0%
16 1
 
10.0%
0 5
50.0%

16
Categorical

High correlation 

Distinct5
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size718.0 B
0
87
204
199
33

Length

Max length3
Median length1
Mean length1.6
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)40.0%

Sample

1st row0
2nd row87
3rd row0
4th row204
5th row0

Common Values

ValueCountFrequency (%)
0 6
60.0%
87 1
 
10.0%
204 1
 
10.0%
199 1
 
10.0%
33 1
 
10.0%

Length

2025-03-23T20:14:10.141316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T20:14:10.184567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6
60.0%
87 1
 
10.0%
204 1
 
10.0%
199 1
 
10.0%
33 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 7
43.8%
3 2
 
12.5%
9 2
 
12.5%
8 1
 
6.2%
2 1
 
6.2%
7 1
 
6.2%
1 1
 
6.2%
4 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7
43.8%
3 2
 
12.5%
9 2
 
12.5%
8 1
 
6.2%
2 1
 
6.2%
7 1
 
6.2%
1 1
 
6.2%
4 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7
43.8%
3 2
 
12.5%
9 2
 
12.5%
8 1
 
6.2%
2 1
 
6.2%
7 1
 
6.2%
1 1
 
6.2%
4 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7
43.8%
3 2
 
12.5%
9 2
 
12.5%
8 1
 
6.2%
2 1
 
6.2%
7 1
 
6.2%
1 1
 
6.2%
4 1
 
6.2%

485
Real number (ℝ)

High correlation  Unique  Zeros 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.8
Minimum0
Maximum231
Zeros1
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:10.245852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.45
Q1100.5
median193.5
Q3217.75
95-th percentile228.75
Maximum231
Range231
Interquartile range (IQR)117.25

Descriptive statistics

Standard deviation87.156054
Coefficient of variation (CV)0.57795792
Kurtosis-0.82208428
Mean150.8
Median Absolute Deviation (MAD)35
Skewness-0.87247784
Sum1508
Variance7596.1778
MonotonicityNot monotonic
2025-03-23T20:14:10.295282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
214 1
10.0%
198 1
10.0%
96 1
10.0%
114 1
10.0%
226 1
10.0%
189 1
10.0%
219 1
10.0%
231 1
10.0%
0 1
10.0%
21 1
10.0%
ValueCountFrequency (%)
0 1
10.0%
21 1
10.0%
96 1
10.0%
114 1
10.0%
189 1
10.0%
198 1
10.0%
214 1
10.0%
219 1
10.0%
226 1
10.0%
231 1
10.0%
ValueCountFrequency (%)
231 1
10.0%
226 1
10.0%
219 1
10.0%
214 1
10.0%
198 1
10.0%
189 1
10.0%
114 1
10.0%
96 1
10.0%
21 1
10.0%
0 1
10.0%

150
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.2
Minimum0
Maximum238
Zeros4
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:10.343004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median95
Q3196.25
95-th percentile225.85
Maximum238
Range238
Interquartile range (IQR)196.25

Descriptive statistics

Standard deviation98.042848
Coefficient of variation (CV)0.97847153
Kurtosis-1.8060907
Mean100.2
Median Absolute Deviation (MAD)95
Skewness0.21379564
Sum1002
Variance9612.4
MonotonicityNot monotonic
2025-03-23T20:14:10.389096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 4
40.0%
211 2
20.0%
101 1
 
10.0%
89 1
 
10.0%
152 1
 
10.0%
238 1
 
10.0%
ValueCountFrequency (%)
0 4
40.0%
89 1
 
10.0%
101 1
 
10.0%
152 1
 
10.0%
211 2
20.0%
238 1
 
10.0%
ValueCountFrequency (%)
238 1
 
10.0%
211 2
20.0%
152 1
 
10.0%
101 1
 
10.0%
89 1
 
10.0%
0 4
40.0%

167
Categorical

Constant 

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size712.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10
100.0%

Length

2025-03-23T20:14:10.446341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T20:14:10.470052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 10
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10
100.0%

590
Categorical

High correlation 

Distinct4
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size718.0 B
0
183
167
223

Length

Max length3
Median length1
Mean length1.6
Min length1

Characters and Unicode

Total characters16
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)30.0%

Sample

1st row183
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7
70.0%
183 1
 
10.0%
167 1
 
10.0%
223 1
 
10.0%

Length

2025-03-23T20:14:10.530855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T20:14:10.580683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 7
70.0%
183 1
 
10.0%
167 1
 
10.0%
223 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 7
43.8%
1 2
 
12.5%
3 2
 
12.5%
2 2
 
12.5%
8 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7
43.8%
1 2
 
12.5%
3 2
 
12.5%
2 2
 
12.5%
8 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7
43.8%
1 2
 
12.5%
3 2
 
12.5%
2 2
 
12.5%
8 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7
43.8%
1 2
 
12.5%
3 2
 
12.5%
2 2
 
12.5%
8 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%

204
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.8
Minimum0
Maximum220
Zeros5
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:10.628148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q3129.75
95-th percentile217.75
Maximum220
Range220
Interquartile range (IQR)129.75

Descriptive statistics

Standard deviation92.643642
Coefficient of variation (CV)1.3664254
Kurtosis-0.84669202
Mean67.8
Median Absolute Deviation (MAD)20
Skewness1.0231174
Sum678
Variance8582.8444
MonotonicityNot monotonic
2025-03-23T20:14:10.684223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5
50.0%
215 1
 
10.0%
158 1
 
10.0%
45 1
 
10.0%
40 1
 
10.0%
220 1
 
10.0%
ValueCountFrequency (%)
0 5
50.0%
40 1
 
10.0%
45 1
 
10.0%
158 1
 
10.0%
215 1
 
10.0%
220 1
 
10.0%
ValueCountFrequency (%)
220 1
 
10.0%
215 1
 
10.0%
158 1
 
10.0%
45 1
 
10.0%
40 1
 
10.0%
0 5
50.0%

673
Categorical

High correlation 

Distinct3
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size715.0 B
0
128
45

Length

Max length3
Median length1
Mean length1.3
Min length1

Characters and Unicode

Total characters13
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8
80.0%
128 1
 
10.0%
45 1
 
10.0%

Length

2025-03-23T20:14:10.738547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T20:14:10.785938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8
80.0%
128 1
 
10.0%
45 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 8
61.5%
1 1
 
7.7%
2 1
 
7.7%
8 1
 
7.7%
4 1
 
7.7%
5 1
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8
61.5%
1 1
 
7.7%
2 1
 
7.7%
8 1
 
7.7%
4 1
 
7.7%
5 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8
61.5%
1 1
 
7.7%
2 1
 
7.7%
8 1
 
7.7%
4 1
 
7.7%
5 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8
61.5%
1 1
 
7.7%
2 1
 
7.7%
8 1
 
7.7%
4 1
 
7.7%
5 1
 
7.7%

135
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.7
Minimum0
Maximum222
Zeros4
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:10.820578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q385.75
95-th percentile186
Maximum222
Range222
Interquartile range (IQR)85.75

Descriptive statistics

Standard deviation76.441772
Coefficient of variation (CV)1.3723837
Kurtosis1.2133001
Mean55.7
Median Absolute Deviation (MAD)15
Skewness1.3762692
Sum557
Variance5843.3444
MonotonicityNot monotonic
2025-03-23T20:14:10.876868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4
40.0%
3 1
 
10.0%
222 1
 
10.0%
90 1
 
10.0%
73 1
 
10.0%
142 1
 
10.0%
27 1
 
10.0%
ValueCountFrequency (%)
0 4
40.0%
3 1
 
10.0%
27 1
 
10.0%
73 1
 
10.0%
90 1
 
10.0%
142 1
 
10.0%
222 1
 
10.0%
ValueCountFrequency (%)
222 1
 
10.0%
142 1
 
10.0%
90 1
 
10.0%
73 1
 
10.0%
27 1
 
10.0%
3 1
 
10.0%
0 4
40.0%

581
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.7
Minimum0
Maximum224
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:10.920703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.25
median62
Q3103
95-th percentile218.6
Maximum224
Range224
Interquartile range (IQR)100.75

Descriptive statistics

Standard deviation85.559661
Coefficient of variation (CV)1.1155106
Kurtosis-0.51254323
Mean76.7
Median Absolute Deviation (MAD)59.5
Skewness0.86783888
Sum767
Variance7320.4556
MonotonicityNot monotonic
2025-03-23T20:14:10.960176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2
20.0%
212 1
10.0%
105 1
10.0%
89 1
10.0%
97 1
10.0%
35 1
10.0%
224 1
10.0%
3 1
10.0%
2 1
10.0%
ValueCountFrequency (%)
0 2
20.0%
2 1
10.0%
3 1
10.0%
35 1
10.0%
89 1
10.0%
97 1
10.0%
105 1
10.0%
212 1
10.0%
224 1
10.0%
ValueCountFrequency (%)
224 1
10.0%
212 1
10.0%
105 1
10.0%
97 1
10.0%
89 1
10.0%
35 1
10.0%
3 1
10.0%
2 1
10.0%
0 2
20.0%

536
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.1
Minimum0
Maximum224
Zeros4
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:11.020671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13.5
Q3185.5
95-th percentile223.1
Maximum224
Range224
Interquartile range (IQR)185.5

Descriptive statistics

Standard deviation103.89038
Coefficient of variation (CV)1.2353196
Kurtosis-2.0832787
Mean84.1
Median Absolute Deviation (MAD)13.5
Skewness0.52153933
Sum841
Variance10793.211
MonotonicityNot monotonic
2025-03-23T20:14:11.057947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4
40.0%
222 1
 
10.0%
187 1
 
10.0%
26 1
 
10.0%
224 1
 
10.0%
1 1
 
10.0%
181 1
 
10.0%
ValueCountFrequency (%)
0 4
40.0%
1 1
 
10.0%
26 1
 
10.0%
181 1
 
10.0%
187 1
 
10.0%
222 1
 
10.0%
224 1
 
10.0%
ValueCountFrequency (%)
224 1
 
10.0%
222 1
 
10.0%
187 1
 
10.0%
181 1
 
10.0%
26 1
 
10.0%
1 1
 
10.0%
0 4
40.0%

576
Real number (ℝ)

High correlation  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.6
Minimum19
Maximum231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:11.120576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile29.8
Q1100.25
median190
Q3216.75
95-th percentile229.2
Maximum231
Range212
Interquartile range (IQR)116.5

Descriptive statistics

Standard deviation79.208866
Coefficient of variation (CV)0.50905441
Kurtosis-0.9154251
Mean155.6
Median Absolute Deviation (MAD)39
Skewness-0.84069197
Sum1556
Variance6274.0444
MonotonicityNot monotonic
2025-03-23T20:14:11.150870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
227 1
10.0%
210 1
10.0%
87 1
10.0%
140 1
10.0%
219 1
10.0%
185 1
10.0%
43 1
10.0%
231 1
10.0%
195 1
10.0%
19 1
10.0%
ValueCountFrequency (%)
19 1
10.0%
43 1
10.0%
87 1
10.0%
140 1
10.0%
185 1
10.0%
195 1
10.0%
210 1
10.0%
219 1
10.0%
227 1
10.0%
231 1
10.0%
ValueCountFrequency (%)
231 1
10.0%
227 1
10.0%
219 1
10.0%
210 1
10.0%
195 1
10.0%
185 1
10.0%
140 1
10.0%
87 1
10.0%
43 1
10.0%
19 1
10.0%

544
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.3
Minimum0
Maximum231
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:11.198167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129.75
median134
Q3214.5
95-th percentile225.6
Maximum231
Range231
Interquartile range (IQR)184.75

Descriptive statistics

Standard deviation97.773037
Coefficient of variation (CV)0.79945247
Kurtosis-2.0801081
Mean122.3
Median Absolute Deviation (MAD)84
Skewness-0.17576739
Sum1223
Variance9559.5667
MonotonicityNot monotonic
2025-03-23T20:14:11.246143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2
20.0%
217 1
10.0%
207 1
10.0%
62 1
10.0%
88 1
10.0%
219 1
10.0%
180 1
10.0%
19 1
10.0%
231 1
10.0%
ValueCountFrequency (%)
0 2
20.0%
19 1
10.0%
62 1
10.0%
88 1
10.0%
180 1
10.0%
207 1
10.0%
217 1
10.0%
219 1
10.0%
231 1
10.0%
ValueCountFrequency (%)
231 1
10.0%
219 1
10.0%
217 1
10.0%
207 1
10.0%
180 1
10.0%
88 1
10.0%
62 1
10.0%
19 1
10.0%
0 2
20.0%

368
Categorical

High correlation 

Distinct4
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size716.0 B
0
1
197
255

Length

Max length3
Median length1
Mean length1.4
Min length1

Characters and Unicode

Total characters14
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)30.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7
70.0%
1 1
 
10.0%
197 1
 
10.0%
255 1
 
10.0%

Length

2025-03-23T20:14:11.309318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T20:14:11.536041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 7
70.0%
1 1
 
10.0%
197 1
 
10.0%
255 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 7
50.0%
1 2
 
14.3%
5 2
 
14.3%
9 1
 
7.1%
7 1
 
7.1%
2 1
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7
50.0%
1 2
 
14.3%
5 2
 
14.3%
9 1
 
7.1%
7 1
 
7.1%
2 1
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7
50.0%
1 2
 
14.3%
5 2
 
14.3%
9 1
 
7.1%
7 1
 
7.1%
2 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7
50.0%
1 2
 
14.3%
5 2
 
14.3%
9 1
 
7.1%
7 1
 
7.1%
2 1
 
7.1%

label
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3
Minimum0
Maximum9
Zeros3
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size142.0 B
2025-03-23T20:14:11.605141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median2.5
Q35
95-th percentile8.1
Maximum9
Range9
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.12872
Coefficient of variation (CV)0.94809697
Kurtosis-0.64664971
Mean3.3
Median Absolute Deviation (MAD)2.5
Skewness0.60513662
Sum33
Variance9.7888889
MonotonicityNot monotonic
2025-03-23T20:14:11.671876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 3
30.0%
2 2
20.0%
5 2
20.0%
9 1
 
10.0%
3 1
 
10.0%
7 1
 
10.0%
ValueCountFrequency (%)
0 3
30.0%
2 2
20.0%
3 1
 
10.0%
5 2
20.0%
7 1
 
10.0%
9 1
 
10.0%
ValueCountFrequency (%)
9 1
 
10.0%
7 1
 
10.0%
5 2
20.0%
3 1
 
10.0%
2 2
20.0%
0 3
30.0%

Interactions

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2025-03-23T20:14:00.189032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:01.016795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:01.811684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:02.912178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:03.690092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-23T20:13:58.234004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:59.027818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:59.823692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:00.680756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:01.486817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:02.327063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:03.372037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:04.169457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:04.977077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.754752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:06.556008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:07.324840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:08.397229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:54.783984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:55.619565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:56.458506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:57.460853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:58.281753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:59.075067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:59.889147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:00.726236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:01.536233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:02.376398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:03.419719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:04.228236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.024747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.800363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:06.603483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:07.368702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:08.444702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:54.827944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:55.666722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:56.490671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:57.508787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:58.329408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:59.122860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:59.936906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:00.773785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:01.584352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:02.658808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:03.451336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:04.280641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.071741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.859056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:06.641306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:07.398532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:08.482798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:54.877183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:55.712305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:56.561141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:57.559751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:58.380815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:59.180124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:59.983022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:00.823240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:01.631896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:02.712185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:03.512917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:04.333264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.117285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.893488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:06.687829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:07.448356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:08.523902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:54.949141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:55.750214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:56.608180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:57.603042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:58.424381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:59.219644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:00.053994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:00.876439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:01.680045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:02.753798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:03.548333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:04.374372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.153843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.940659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:06.731613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:07.500713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:08.571594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:55.010707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:55.794004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:56.653888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:57.655413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:58.459550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:59.265352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:00.098016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:00.918941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:01.711600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:02.812144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:03.600692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:04.413642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.196772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.991701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:06.765488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:07.544652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:08.618800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:55.054386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:55.844460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:56.697536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:57.708879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:58.508759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:13:59.315220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:00.144043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:00.971914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:01.767075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:02.861120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:03.651361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:04.457602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:05.257987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:06.037294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:06.817877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T20:14:07.573511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-23T20:14:11.796233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
1041351501620423333035336838148553654457658159590619632648668673label
1041.0000.5320.5470.8940.7400.3800.4070.3080.8160.2980.2380.3680.3980.3380.7590.7070.4580.6160.5320.3870.4270.000-0.194
1350.5321.0000.0390.6160.2400.0320.474-0.0790.816-0.056-0.2060.2870.0060.3130.4700.7070.4580.6160.1330.4070.4070.756-0.064
1500.5470.0391.0000.7330.8430.9460.1000.5780.6170.3840.508-0.0100.6920.4770.2490.0000.0000.0000.768-0.0800.0200.000-0.681
160.8940.6160.7331.0000.7420.3160.0000.0000.9130.4330.3230.0000.6710.0000.5360.7910.0000.3870.2960.5350.0000.0000.000
2040.7400.2400.8430.7421.0000.8050.0210.6310.5770.4470.511-0.0800.6290.5370.4860.7910.0000.0000.779-0.117-0.0210.000-0.560
2330.3800.0320.9460.3160.8051.000-0.0200.6850.0000.4980.595-0.1460.7690.5830.1850.0000.0000.0000.826-0.268-0.1370.000-0.738
3300.4070.4740.1000.0000.021-0.0201.0000.0520.4580.2720.1230.9010.2720.4980.6030.7070.8160.8940.2420.8620.9590.7560.382
3530.308-0.0790.5780.0000.6310.6850.0521.0000.0000.8750.9760.1670.8200.6280.4460.0000.0000.0000.784-0.1630.0070.267-0.273
3680.8160.8160.6170.9130.5770.0000.4580.0001.0000.0000.2670.0000.5350.0000.2180.8660.2020.5980.0000.6310.4580.0000.000
3810.298-0.0560.3840.4330.4470.4980.2720.8750.0001.0000.9000.3320.8780.7660.6040.7070.0000.0000.8060.0190.2270.000-0.037
4850.238-0.2060.5080.3230.5110.5950.1230.9760.2670.9001.0000.2810.8020.5880.4320.3540.0000.0000.730-0.0710.0970.463-0.130
5360.3680.287-0.0100.000-0.080-0.1460.9010.1670.0000.3320.2811.0000.2010.2810.6020.3540.4630.5770.1200.8470.9140.0000.541
5440.3980.0060.6920.6710.6290.7690.2720.8200.5350.8780.8020.2011.0000.8270.4630.7070.0000.0000.9660.0190.2140.000-0.433
5760.3380.3130.4770.0000.5370.5830.4980.6280.0000.7660.5880.2810.8271.0000.5710.6120.0000.0000.8410.1620.3810.655-0.179
5810.7590.4700.2490.5360.4860.1850.6030.4460.2180.6040.4320.6020.4630.5711.0000.0000.0000.0000.4920.3700.5380.0000.242
590.7070.7070.0000.7910.7910.0000.7070.0000.8660.7070.3540.3540.7070.6120.0001.0000.0000.7910.0000.9350.7070.0000.000
5900.4580.4580.0000.0000.0000.0000.8160.0000.2020.0000.0000.4630.0000.0000.0000.0001.0000.9130.0000.4630.8160.4870.309
6190.6160.6160.0000.3870.0000.0000.8940.0000.5980.0000.0000.5770.0000.0000.0000.7910.9131.0000.0000.8450.8940.3340.296
6320.5320.1330.7680.2960.7790.8260.2420.7840.0000.8060.7300.1200.9660.8410.4920.0000.0000.0001.0000.0200.1900.000-0.506
6480.3870.407-0.0800.535-0.117-0.2680.862-0.1630.6310.019-0.0710.8470.0190.1620.3700.9350.4630.8450.0201.0000.9590.0000.428
6680.4270.4070.0200.000-0.021-0.1370.9590.0070.4580.2270.0970.9140.2140.3810.5380.7070.8160.8940.1900.9591.0000.7560.421
6730.0000.7560.0000.0000.0000.0000.7560.2670.0000.0000.4630.0000.0000.6550.0000.0000.4870.3340.0000.0000.7561.0000.000
label-0.194-0.064-0.6810.000-0.560-0.7380.382-0.2730.000-0.037-0.1300.541-0.433-0.1790.2420.0000.3090.296-0.5060.4280.4210.0001.000

Missing values

2025-03-23T20:14:08.731841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-23T20:14:08.840314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

31066859632038123310433035361964816485150167590204673135581536576544368label
00192019402401422420319321202140018300321222222721709
1000210020420520002040087198101002150222105021020710
20008708893008900096890000000878800
3000137010415612501100020411415200158008901406203
40002190218254002220002262110045000021921900
50991190019021617719120251831991892110040090971871851801972
6000001951002040002190000003526431907
70229023302282272352512272422263323123801672200732242242312312552
8050000010111001600000012814231195005
9016500000457502402290210022304527218119005
31066859632038123310433035361964816485150167590204673135581536576544368label
00192019402401422420319321202140018300321222222721709
1000210020420520002040087198101002150222105021020710
20008708893008900096890000000878800
3000137010415612501100020411415200158008901406203
40002190218254002220002262110045000021921900
50991190019021617719120251831991892110040090971871851801972
6000001951002040002190000003526431907
70229023302282272352512272422263323123801672200732242242312312552
8050000010111001600000012814231195005
9016500000457502402290210022304527218119005